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dc.creatorGhalwash, MF
dc.creatorObradovic, Z
dc.date.accessioned2021-01-31T21:14:22Z
dc.date.available2021-01-31T21:14:22Z
dc.date.issued2012-08-08
dc.identifier.issn1471-2105
dc.identifier.issn1471-2105
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/5435
dc.identifier.other22873729 (pubmed)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/5453
dc.description.abstractBackground: Early classification of time series is beneficial for biomedical informatics problems such including, but not limited to, disease change detection. Early classification can be of tremendous help by identifying the onset of a disease before it has time to fully take hold. In addition, extracting patterns from the original time series helps domain experts to gain insights into the classification results. This problem has been studied recently using time series segments called shapelets. In this paper, we present a method, which we call Multivariate Shapelets Detection (MSD), that allows for early and patient-specific classification of multivariate time series. The method extracts time series patterns, called multivariate shapelets, from all dimensions of the time series that distinctly manifest the target class locally. The time series were classified by searching for the earliest closest patterns.Results: The proposed early classification method for multivariate time series has been evaluated on eight gene expression datasets from viral infection and drug response studies in humans. In our experiments, the MSD method outperformed the baseline methods, achieving highly accurate classification by using as little as 40%-64% of the time series. The obtained results provide evidence that using conventional classification methods on short time series is not as accurate as using the proposed methods specialized for early classification.Conclusion: For the early classification task, we proposed a method called Multivariate Shapelets Detection (MSD), which extracts patterns from all dimensions of the time series. We showed that the MSD method can classify the time series early by using as little as 40%-64% of the time series' length. © 2012 Ghalwash and Obradovic; licensee BioMed Central Ltd.
dc.format.extent195-
dc.language.isoen
dc.relation.haspartBMC Bioinformatics
dc.relation.isreferencedbySpringer Science and Business Media LLC
dc.rightsCC BY
dc.rights.urihttp://creativecommons.org/licenses/by/2.0
dc.subjectAlgorithms
dc.subjectClassification
dc.subjectGene Expression
dc.subjectHumans
dc.subjectInfluenza, Human
dc.subjectMedical Informatics
dc.subjectMultiple Sclerosis
dc.subjectMultivariate Analysis
dc.titleEarly classification of multivariate temporal observations by extraction of interpretable shapelets
dc.typeArticle
dc.type.genreJournal Article
dc.relation.doi10.1186/1471-2105-13-195
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.date.updated2021-01-31T21:14:19Z
refterms.dateFOA2021-01-31T21:14:23Z


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